Bhovix Lab AI systems studio
I build AI agents, business workflows and self-healing cloud systems. You get a working pilot in about 14 days, on your real data.
What I build four kinds of system
02 agents
Support triage, booking, sales follow-up and internal assistants. The agent asks a person before any important action.
Example A support agent reads each ticket, finds the order, writes the reply and waits for your OK.
03 workflows
I connect your CRM, accounting, e-commerce, email and databases. Data moves by itself. Nobody copies and pastes.
Example A new order creates the invoice, updates the stock and tells the right team.
04 documents
Invoices, PDFs, forms and scans become checked data in your system. Unclear fields go to a person.
Example Supplier invoices are read, matched to purchase orders and posted. Exceptions are flagged.
05 cloud
When an alert starts, the system finds the cause and proposes one safe fix. It applies the fix after approval, checks the result and can undo it.
Example This is how WARDEN works. You can read its code today.
Use cases where it helps
A few examples of systems I build. Each one is shaped around your tools, your data and your rules.
What you get every project
Running on your real data, in a safe test space, in about two weeks.
Real questions with known answers. The system must reach the agreed pass rate before release.
What the system costs each month at your volume, based on real usage numbers.
Logs, alerts and simple dashboards, so problems show up early.
How to run it, what to watch, its known limits and who to call.
How a build works five steps
Pilots from $1,500
A fixed price for each step, agreed before work starts. No surprises.
You show me the manual task. I ask about your data and your tools.
You get a fixed price, a written scope and clear acceptance tests.
A working pilot runs on your real data in a safe test space.
We add the edge cases and connect the rest. Each sprint has its own fixed price.
You get the code, the documents and a short video. I fix bugs free for 30 days.
Safety by design eight steps
AI is fast, but it can be wrong. So every system I build follows the same eight steps.
Every input is checked for hidden instructions.
The AI suggests one action from a fixed list.
Plain rules, not the AI, decide if the action is allowed.
A person approves each important action.
The system applies the change one time.
It checks that the problem is gone.
If the problem stays, it reverts the change.
Every step is signed and kept.
Proof open source
These are my own public systems. Client case studies follow.
An open-source AI on-call engineer for AWS.
An alarm comes in. WARDEN reads the evidence. The AI proposes one fix from a fixed list. Plain rules decide if the fix is allowed. The fix is applied only after approval. Then WARDEN checks the result, undoes the fix if needed and signs every step.
Read the WARDEN codeSee what breaks before you apply Terraform.
A Rust and Z3 engine. It simulates a failure on your AWS design and shows which services break, before the change goes live. Any fix that an AI proposes is checked again by the engine.
Read the Helios codeWho builds it one person
I am Veera Rakesh Kethari. Bhovix Lab is my independent studio.
Before this, I was a DevOps engineer at Winmore. I built its first Terraform estate: five AWS environments and more than 1,000 resources. I closed SOC 2 Type II findings. I led incident analysis on Datadog, Grafana and CloudWatch.
Now I use that production experience to build AI systems that are safe to run every day.
Questions before you write
AI assistants and agents for support, sales and operations. Automations that connect the tools you already use. Document pipelines that turn invoices, forms and PDFs into clean data. Cloud and DevOps automation that finds and fixes problems safely. If a task is manual, repeated and rule-based, it is usually a good fit.
Most tools with an API: CRMs such as HubSpot and Salesforce, accounting and ERP tools, Shopify and other e-commerce platforms, Slack, email, Google Workspace, databases, and AWS, Google Cloud or Azure. I pick the AI model that fits the job: Claude, OpenAI, Gemini or a private model.
Before anything goes live, I build a set of real test questions with known good answers. Every version of the system must pass them. Answers cite their source, and the system says it does not know when it is not sure. Important actions wait for a person to approve them.
The system runs in your own cloud account where possible. Sensitive values are masked before an AI model sees them. Each part gets only the access it needs. Inputs are checked for hidden instructions, and every action is logged.
A working pilot on your real data usually takes about two weeks. Production follows in short, planned steps. Each step has clear results that we agree on first, so you always know what comes next.
Yes. I adapt to your working hours for calls, launches and urgent issues. Day to day, you get short written updates and videos, so you can see progress whenever it suits you.
You get monitoring, a clear runbook for your team and a short handover video. I stay available to improve the system, add new workflows or support it over time.
Yes. I can build alongside your engineers, work through your normal reviews and tools, or deliver a complete system that your team then runs.
Start one task
Tell me what takes your team hours each week. I reply within one working day with a first idea and a price range.
Message me on LinkedIn